A Partition-Based Active Contour Model Incorporating Local Information for Image Segmentation
Active contour models are always designed on the assumption that images are approximated by regions with piecewise-constant intensities. This assumption, however, cannot be satisfied when describing intensity inhomogeneous images which frequently occur in real world images and induced considerable d...
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doaj-539a32a458484b989469d73579d8cfee2020-11-25T01:38:42ZengHindawi LimitedThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/840305840305A Partition-Based Active Contour Model Incorporating Local Information for Image SegmentationJiao Shi0Jiaji Wu1Anand Paul2Licheng Jiao3Maoguo Gong4Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Institute of Intelligent Information Processing, Xidian University, Xi’an, Shaanxi 710071, ChinaKey Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Institute of Intelligent Information Processing, Xidian University, Xi’an, Shaanxi 710071, ChinaSchool of Computer Science Engineering, Kyungpook National University, Daegu 702-701, Republic of KoreaKey Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Institute of Intelligent Information Processing, Xidian University, Xi’an, Shaanxi 710071, ChinaKey Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Institute of Intelligent Information Processing, Xidian University, Xi’an, Shaanxi 710071, ChinaActive contour models are always designed on the assumption that images are approximated by regions with piecewise-constant intensities. This assumption, however, cannot be satisfied when describing intensity inhomogeneous images which frequently occur in real world images and induced considerable difficulties in image segmentation. A milder assumption that the image is statistically homogeneous within different local regions may better suit real world images. By taking local image information into consideration, an enhanced active contour model is proposed to overcome difficulties caused by intensity inhomogeneity. In addition, according to curve evolution theory, only the region near contour boundaries is supposed to be evolved in each iteration. We try to detect the regions near contour boundaries adaptively for satisfying the requirement of curve evolution theory. In the proposed method, pixels within a selected region near contour boundaries have the opportunity to be updated in each iteration, which enables the contour to be evolved gradually. Experimental results on synthetic and real world images demonstrate the advantages of the proposed model when dealing with intensity inhomogeneity images.http://dx.doi.org/10.1155/2014/840305 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jiao Shi Jiaji Wu Anand Paul Licheng Jiao Maoguo Gong |
spellingShingle |
Jiao Shi Jiaji Wu Anand Paul Licheng Jiao Maoguo Gong A Partition-Based Active Contour Model Incorporating Local Information for Image Segmentation The Scientific World Journal |
author_facet |
Jiao Shi Jiaji Wu Anand Paul Licheng Jiao Maoguo Gong |
author_sort |
Jiao Shi |
title |
A Partition-Based Active Contour Model Incorporating Local Information for Image Segmentation |
title_short |
A Partition-Based Active Contour Model Incorporating Local Information for Image Segmentation |
title_full |
A Partition-Based Active Contour Model Incorporating Local Information for Image Segmentation |
title_fullStr |
A Partition-Based Active Contour Model Incorporating Local Information for Image Segmentation |
title_full_unstemmed |
A Partition-Based Active Contour Model Incorporating Local Information for Image Segmentation |
title_sort |
partition-based active contour model incorporating local information for image segmentation |
publisher |
Hindawi Limited |
series |
The Scientific World Journal |
issn |
2356-6140 1537-744X |
publishDate |
2014-01-01 |
description |
Active contour models are always designed on the assumption that images are approximated by regions with piecewise-constant intensities. This assumption, however, cannot be satisfied when describing intensity inhomogeneous images which frequently occur in real world images and induced considerable difficulties in image segmentation. A milder assumption that the image is statistically homogeneous within different local regions may better suit real world images. By taking local image information into consideration, an enhanced active contour model is proposed to overcome difficulties caused by intensity inhomogeneity. In addition, according to curve evolution theory, only the region near contour boundaries is supposed to be evolved in each iteration. We try to detect the regions near contour boundaries adaptively for satisfying the requirement of curve evolution theory. In the proposed method, pixels within a selected region near contour boundaries have the opportunity to be updated in each iteration, which enables the contour to be evolved gradually. Experimental results on synthetic and real world images demonstrate the advantages of the proposed model when dealing with intensity inhomogeneity images. |
url |
http://dx.doi.org/10.1155/2014/840305 |
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